Chen-Wang-CUHK

Chen-Wang-CUHK's Stars

• huggingface/neuralcoref

  ✨Fast Coreference Resolution in spaCy with Neural Networks

  Language:C2.9k97312478

• nlpyang/BertSum

  Code for paper Fine-tune BERT for Extractive Summarization

  Language:Python1.5k32121424

• jiangxinyang227/textClassifier

  tensorflow implementation

  Language:Jupyter Notebook1.1k3521556

• yuewang-cuhk/awesome-vision-language-pretraining-papers

  Recent Advances in Vision and Language PreTrained Models (VL-PTMs)

  1.1k537104

• ChenRocks/fast_abs_rl

  Code for ACL 2018 paper: "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting. Chen and Bansal"

  Language:Python6252267186

• maszhongming/MatchSum

  Code for ACL 2020 paper: "Extractive Summarization as Text Matching"

  Language:Python521937107

• facebookresearch/EmpatheticDialogues

  Dialogue model that produces empathetic responses when trained on the EmpatheticDialogues dataset.

  Language:Python454124264

• thunlp/BERT-KPE

  Language:Python446161778

• bheinzerling/pyrouge

  A Python wrapper for the ROUGE summarization evaluation package

  Language:Python25143171

• crabcamp/lexrank

  LexRank algorithm for text summarization

  Language:Python2294542

• lipiji/neural-summ-cnndm-pytorch

  Neural abstractive summarization (seq2seq + copy (or pointer network) + coverage) in pytorch on CNN/Daily Mail

  Language:Python19910938

• microsoft/OpenKP

  Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.

  Language:Python155161029

• lipiji/TranSummar

  Transformer for abstractive summarization on cnn/daily-mail and gigawords

  Language:Python14092020

• nlpdata/dialogre

  Dialogue-Based Relation Extraction

  Language:Python13410031

• ChenRocks/Distill-BERT-Textgen

  Research code for ACL 2020 paper: "Distilling Knowledge Learned in BERT for Text Generation".

  Language:Python13011619

• henryhungle/MTN

  Code for the paper Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (ACL19)

  Language:Python9861325

• thunlp/GEAR

  Source code for ACL 2019 paper "GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification"

  Language:Python9882325

• Yifan-Gao/conversational-QG

  [ACL 2019]: Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling

  Language:Python886621

• 52paper/52paper.github.io

  7431014

• MiuLab/DuaLUG

  The implementation of the papers on dual learning of natural language understanding and generation. (ACL2019,2020; Findings of EMNLP 2020)

  Language:Python664317

• wszlong/sb-nmt

  Code for Synchronous Bidirectional Neural Machine Translation (SB-NMT)

  Language:Python643717

• Chen-Wang-CUHK/ExHiRD-DKG

  The source code for ACL 2020 paper Exclusive Hierarchical Decoding for Deep Keyphrase Generation

  Language:Python56384

• MiuLab/DialSum

  Dialogue Summarization

  Language:Python525612

• ddkang/loss_dropper

  Language:Python51469

• ygorg/KPTimes

  Repository for KPTimes corpus

  Language:Jupyter Notebook34354

• HKUST-KnowComp/Visual_PCR

  Dataset and Source code for EMNLP 2019 paper "What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues"

  Language:Python25542

• SVAIGBA/CDKGen

  Language:Python23461

• kenchan0226/dual_view_review_sum

  Code for the SIGIR 2020 paper "A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss"

  Language:Python21232

• falcondai/pyrouge

  A Python wrapper for the ROUGE summarization evaluation package

  Language:HTML14204

• sebastianGehrmann/rouge-baselines

  summarization baselines and their ROUGE scores

  Language:Python8106